Software quality management lecture notes

A.V.C.COLLEGE OF ENGINEERING
MANNAMPANDAL, MAYILADUTHURAI-609 305

COURSE MATERIAL
FOR THE SUBJECT OF
SOFTWARE QUALITY MANAGEMENT

SUB NAME

:

SOFTWARE QUALITY MANAGEMENT

SEM

:

VIII

DEPARTMENT

:

COMPUTER SCIENCE AND ENGINEERING

ACADEMIC YEAR

:

2012-2013

NAME OF THE FACULTY :

PARVATHI.M

DESIGNATION

Asst.Professor

:

A.V.C.COLLEGE OF ENGINEERING
MANNAMPANDAL, MAYILADUTHURAI-609 305
Syllabus
CS1023 – SOFTWARE QUALITY MANAGEMENT

LTPC
3 0 0 3

UNIT I FUNDAMENTALS OF SOFTWARE QUALITY
9
Software quality − Hierarchical models of Boehm and McCall − Quality measurement
− Metrics measurement and analysis − Gilb‘s approach − GQM Model.
UNIT II SOFTWARE QUALITY ASSURANCE
9
Quality tasks − SQA plan − Teams − Characteristics − Implementation −
Documentation − Reviews and audits.
UNIT III QUALITY CONTROL AND RELIABILITY
9
Tools for quality − Ishikawa‘s basic tools − CASE tools − Defect prevention and
removal − Reliability models − Rayleigh model − Reliability growth models
for quality assessment.
UNIT IV QUALITY MANAGEMENT SYSTEM
9
Elements of QMS − Rayleigh model framework − Reliability growth models for
QMS − Complexity metrics and models − Customer satisfaction analysis.
UNIT V QUALITY STANDARDS
9
Need for standards − ISO 9000 Series − ISO 9000-3 for software development −
CMM and CMMI − Six sigma concepts.
Total: 45
TEXT BOOKS
1. Allan C. Gillies, ―Software Quality: Theory and Management‖, Thomson
Learning, 2003.

2. Stephen H. Kan, ―Metrics and Models in Software Quality
Engineering‖, Pearson Education (Singapore) Pvt. Ltd., 2002.
REFERENCES
1. Norman E. Fenton and Shari Lawrence Pfleeger, ―Software
Metrics‖, Thomson, 2003.
2. Mordechai Ben, Menachem and Garry S.Marliss, ―Software
Quality‖ Thomson Asia Pvt. Ltd., 2003.
3. ISO 9000-3, ―Notes for the Application of the ISO 9001 Standard to software
development‖.
4. Kamna Malik and Praveen Choudry, ―Software Quality : A Practitioner
Approach‖, PHI, 2000

UNIT 1

FUNDAMENTALS OF SOFTWARE QUALITY

Contents:
· Views of Quality
· Hierarchical Modeling
· Boehm and McCall‘s Models
· Quality Criteria
· Interrelation
· Measuring Quality
· Quality Metrics
· Overall Measures of Quality
Quality:
Ability of the product/service to fulfill its function
Hard to define
Impossible to measure
Easy to recognize in its absence
Transparent when present
Definition of Quality:

Characteristics of Quality:
Quality is not absolute
Quality is multidimensional
Quality is subject to constraints
Quality is about acceptable compromises
Quality criteria are not independent, but interact with each other causing
conflicts.

Software Quality:
Kitchen ham (1989 b) refers to software quality ―fitness for needs‖ and claims quality
involves matching expectations.
Two features of a piece of quality software:
-> Conformance to its specification
-> Fitness for its intended purpose.
The Department of Defense (DOD, 1985) in the USA defines software quality as ―the
degree to which the attributes of the software enable it to perform its intended end use‖.

Software product quality
Product quality
o conformance to requirements or program specification; related to Reliability
Scalability
o Correctness
Completeness
absence of bugs
Fault-tolerance
o Extensibility
o Maintainability
Documentation
Software was particularly problematical for the following reasons:
->
->
->
->
->

Software has no physical existence
The lack of knowledge of client needs at the start
The change of client needs over time
The rapid rate of change on both hardware and software
The high expectations of customers, particularly with respect to adaptability.

Within the software quality area, the need to provide a solution that matches user needs is
often considered as ―design quality‖, whilst ensuring a match to the specification is
considered as ―manufacturing quality‖.
Software Quality Factors

A software quality factor is a non-functional requirement for a software program which is
not called up by the customer's contract, but nevertheless is a desirable requirement which
enhances the quality of the software program.
Understandability

Completeness
Maintainability
Conciseness
Portability
Consistency
Testability
Usability
Reliability
Structured ness
Efficiency
Security
Views of Quality:
Quality is a multidimensional construct. It may therefore be considered using a
polyhedron metaphor. Within this metaphor, a three-dimensional solid represents quality.
Each face represents a different aspect of quality such as correctness, reliability, and
efficiency.
It has been classified according to a number of ‗views‘ or perspective. These views are
often diverse and may conflict with each other. Each view comes from a particular
context.
The views are generally presented in adversarial pairs such as versus designers.
The software project has the following roles
Project manager
Business analyst
Implementation programmer
Quality auditor
End user
Line manager
Project sponsor

In an attempt to classify different and conflicting views of quality, Garvin (1984) has
suggested five different views of quality
1. The transcendent view
· Innate excellence
· Classical definition
2. The product-based view
· Higher the quality higher the cost
· Greater functionality
· Greater care in development
3. The user-based view
· Fitness for purpose
· Very hard to quantify
4. The manufacturing view
· Measures quality in terms of conformance
· Zero defects
5. The value-based view
· Provides the data with what the customer
requires at a price.
Quality is determined by people because
· It is people and human organizations who have problems to be solved by
computer software
· It is people who define the problems and specify the solutions
· It is still currently people who implement designs and product code.
· It is people who test code

HIERARCHICAL MODEL OF QUALITY:
To compare quality in different situations, both qualitatively and quantitatively, it is
necessary to establish a model of quality.
Many model suggested for quality.
Most are hierarchical in nature.
A quantitative assessment is generally made, along with a more quantified assessment.
Two principal models of this type, one by Boehm (1978) and
one by McCall in 1977. A hierarchical model of software quality is based upon a set of
quality criteria, each of which has a set of measures or metrics associated with it.
The issues relating to the criteria of quality are:
-> What criteria of quality should be employed?
-> How do they inter-relate?
-> How may the associated metrics be combined into a meaningful overall
measure of Quality?
THE HIERARCHICAL MODELS OF BOEHM AND MCCALL
THE GE MODEL (MCCALL, 1977 AND 1980) / (McCall Model)
-> This model was first proposed by McCall in 1977.
-> It was later revised as the MQ model, and it is aimed by system developers to be
used during the development process.
-> In early attempt to bridge the gap between users and developers, the criteria were
chosen in an attempt to reflect user‘ s views as well as developer‘ s priorities.
-> The criteria appear to be technically oriented, but they are described by a series of
questions which define them in terms to non specialist managers.
The three areas addressed by McCall’ s model (1977):
Product operation: requires that it can be learnt easily, operated efficiently And it results
are those required by the users.

Product revision: it is concerned with error correction and
Adaptation Of the system and it is most costly part of software development.
Product transition: it is an important application and it is
distributed processing and the rapid rate of change in hardware is Likely to increase.
McCall’ s criteria of quality defined
Efficiency is concerned with the use of resources e.g.
processor time, storage. It falls into two categories:
execution efficiency and storage efficiency.
Usability is the ease of use of the software.
Integrity is the protection of the program from
unauthorized access.
Correctness is the extent to which a program fulfils its specification.
Reliability is its ability not to fail.
Maintainability is the effort required to locate and fix a fault in the program
within its operating environment.
Flexibility is the ease of making changes required by
changes in the operating environment.
Testability is the ease of testing the programs, to ensure that it is error-free and
meet its specification.
Portability is the effort required to transfer a program from one environment to
another.
Reusability is the ease of refusing software in a different context.
Interoperability is the effort required to couple the
system to another system.

The Boehm model (1978)
It is to provide a set of well-defined, well-differentiated characteristics of
software quality.
It is hierarchical in nature but the hierarchy is extended, so that quality criteria
are subdivided.
According to the uses made of the system and they are classed into ‗ general‘ or
‗ as is‘ and the utilities are a subtype of the general utilities, to the product
operation.
There are two levels of actual quality criteria, the
intermediate level being further split into primitive
characteristics which are amenable to measurement.
This model is based upon a much larger set of criteria than McCall‘ s model,
but retains the same emphasis on technical criteria.
The two models share a number of common characteristics are,
The quality criteria are supposedly based upon the user‘s
view.
The models focus on the parts that designers can more
readily analyze.
Hierarchical models cannot be tested or validated. It
cannot be shown that the metrics accurately reflect the
criteria.
The measurement of overall quality is achieved by a
weighted summation of the characteristics.
Boehm talks of modifiability where McCall distinguishes expandability from adaptability
and documentation, understandability and clarity.
HOW THE QUALITY CRITERIA INTERRELATE
The individual measure of software quality provided do not provide an over all
measure of software quality.
The individual measures must be combined.
The individual measures of quality may conflict with each
other.
Some of these relationships are described below;
Integrity vs. efficiency (inverse) the control of access to data or software requires
additional code and processing leading to a longer runtime and additional storage
requirement.
Usability vs. efficiency (inverse) Improvements in the human / computer interface may
significantly increase the amount of code and power required.
Maintainability and testability vs. efficiency (inverse) Optimized and compact code is

not easy to maintain.
Portability vs. efficiency (inverse) the use of optimized software or system utilities will
lead to decrease in probability.
Flexibility and reusability vs. integrity (inverse) the general flexible data structures
required for flexible and reusable software increase the security and protection problem.
Interoperability vs. integrity (inverse) Coupled system allow more avenues of access to
more and different users.
Reusability vs. reliability (inverse) reusable software is required to be general:
maintaining accuracy and error tolerance across all cases is difficult.
Maintainability vs. flexibility (direct) maintainable code arises from code that is well
structured.
Maintainability vs. reusability (direct) well structured easily maintainable code is
easier to reuse in other programs either as a library of routines or as code placed directly
within another program.
Portability vs. reusability (direct) portable code is likely to be free of environmentspecific features.
Correctness vs. efficiency (neutral) the correctness of code, i.e. its conformance to
specification does not influence its efficiency.
MEASURING SOFTWARE QUALITY
MEASURING QUALITY
Quality measurement, where it is considered at all, is usually
expressed in terms of metrics.
Software metric is a measurable property which is an indicator of one or more of the
quality criteria that we are seeking to measure. As such, there are a number of conditions
that a quality metric must meet. It must:
-> Be clearly linked to the quality criterion that it seeks to measure
-> Be sensitive to the different degrees of the criterion
-> Provide objective determination of the criterion that can be mapped onto a
suitable scale.
-> Metrics are not same as direct measures.
Measurement techniques applied to software are more akin to the social sciences,
where properties are similarly complex and ambiguous.
A typically measurable property on which a metric may be based is structured ness.
Structured ness as it simplest may be calculated in terms of the average length of code
modules within the programs.

SOFTWARE METRICS
Metrics are classified into two types according to whether they are predictive or
descriptive.
A predictive metric is used to make predictions about the software later in the
lifecycle. Structured ness is used to predict the maintainability of the software product in
use.
A descriptive metric describes the state of the software at
the time of measurement.
Different authors have taken different approaches to metrics.
Structured ness is measured by questions such as:
-> Have the rules for transfer of control between modules been followed?(y/n)
-> Are modules limited in size?(y/n)
-> Do all modules have only one exit point ?(y/n)
-> Do all modules have only one entry point?(y/n)
-> A well-structured program will produce positive answers to such questions.
McCall‘s approach is more quantities, using scores derived from equations such as

Where: n01 = no of modules containing one or zero exit points only ntot = total number
of modules
Generally, in this approach, scores are normalized to a
range between 0 and 1, to allow for easier combination and comparison.
This appears attractive, to give unjustified credibility to the results obtained.
To validate this relationship and determine whether it is a
linear relationship or more complex in nature.
It is also possible to validate whether the dependence of maintainability structured ness in
identical to that of adaptability or reusability.
What makes a good metric?
Seven criteria for a good metric, after Watts (1987)
Objectivity the results should be free from subjective influences. It must not matter
who the measurer is.
Reliability the results should be precise and repeatable.
Validity the metric must measure the correct characteristic.
Standardization the metric must be unambiguous and allow for comparison.

Comparability the metric must be comparable with other measures of the same
Criterion.
Economy the simpler and therefore, the cheaper the measure is to use, the Better.
Usefulness the measure must address a need, not simply measure a property for its own
sake.
A further important feature is consistency.
Automation is also desirable.
Metrics cited in the literature:
Metrics available for each criterion (after Watts, 1987)

The metrics cited depends to a very large extent upon just seven distinct measurable
properties: readability, error prediction, error detection, complexity, and mean time to
failure (MTTF), modularity, testability.
1. Readability as a measure of usability may be applied to documentation in order to
assess how such documentation may assist in the usability of a piece of software.
2. Error prediction as a measure of correctness this measure is depends upon the stable
software development environment.
3. Error detection as measure of correctness

4. Mean time to failure (MTTF) as a measure of reliability
5. Complexity as a measure of reliability the assumption underpinning these measures is
that as complexity increases, so reliability decrease.
6. Complexity as a measure of maintainability is also indicative of maintainability.
7. Readability of code as a measure of maintainability has also been suggested as a
measure of maintainability.
8. Modularity as a measure of maintainability increased modularity is generally assumed
to increase maintainability. Four measures have been suggested. Yau and Collofello
(1979)
measured ― stability‖ as the number of modules affected by program
modification. Kentger (1981) defined a four-level hierarchy of module types:
Control modules.
Problem-oriented modules.
Management modules for abstract data.
Realization modules for abstract data.
9. Testability as a measure of maintainability the ease and effectiveness of testing will
have an impact upon the maintainability of a product.
An overall measure of quality
Much of the work in this area has been concerned with simple reduction of a set of scores
to a single ‗figure-of-merit‘.
Five such methods are detailed by Watts (1987) as part of the MQ approach.
1. Simple scoring: In this method, each criterion is allocated a score. The overall quality
is given by the mean of the individual scores.
2. Weighted scoring: This scheme allows the user to weight each criterion according to
how important they consider them to be. Each criterion is evaluated to produce a score
between 0 and 1. Each score is weighted before summation and the resulting figure
reflects the relative importance if the different factors.
3. Phased weighting factor method: This is an extension of weighted scoring. A
weighting is assigned to a group characteristics before each individual weighting is
considered.

The phased weighting factor method
Product operation weighted mean = 0.660
Product transition weighted mean = 0.633
Overall measure by PWF method = ((2/3) x0.660) + ((1/3) x0.633) = 0.65
4. The Kepner- Tregoe method (1981): The criteria are divided into ‗ essential‘ and ‗
desirable‘ . A minimum value is specified for each essential criterion and any software
failing to reach these scores is designated unsuitable.
‗ Suitable‘ software is then judged by use of the weighting factor method.
5. The Cologne combination method (Schmitz, 1975): This method is designed with
comparative evaluation is mind. Using the chosen criteria, each product is ranked in
order.
POLARITY PROFILING:
In this scheme, quality is represented by series of ranges from -3 to +3.
The required quality may be represented and compared to
the actual quality achieved.
It is a common problem amongst software developers that they focus upon particular
aspects of quality.

When a user complains of poor quality, they tend to improve the product further
in these areas.
Often the product has already exceeded the user‘ s expectations in these areas, and a
further improvement does not improve their overall view of the quality of the product.
This effort wasted.
Worse, the user‘s needs still have not been met in other critical areas, leading
to tensions between the developers and users.
Two different outcomes result.
In the first case, usability is improved. Unfortunately, reliability and efficiency are
still not up to the required standard, and usability was already considered satisfactory.
In the second case, improvements in reliability and efficiency are traded for
a reduction in adaptability and maintainability, perhaps by‗ tweaking‘ the code.
The consequence is that all criteria are now at the required level, resulting in an
overall perception of quality and satisfied users.
The purpose of standard
Standards are generally defined in terms of a model of a best practice against which
all others are compared.
 It is used to build better products
 Also used to ensure whether the products are conformance to a standard
Types of accreditation
Type

Description

First party

internal monitoring

Second party

external monitoring by a customer

Third party

external monitoring by an independent standards body

Benefits of accreditation

It provides external validation to see whether the investment made in the
QMS is being effective
It gives credibility to the supplier and their quality system
It allows the supplier to sell to those customers who insist on
accreditation as a condition of tender
It qualifies the supplier to be included in the buyers guides

THE GOAL QUESTION METRIC APPROACH
The Goal Question Metric (GQM) approach is based upon the assumption that for an
organization to measure in a purposeful way it must first specify the goals for itself and its
projects, then it must trace those goals to the data that are intended to define those goals
operationally, and finally provide a framework for interpreting the data with respect to the
stated goals. Thus it is important to make clear, at least in general terms, what informational
needs the organization has, so that these needs for information can be quantified whenever
possible, and the quantified information can be analyzed a to whether or not the
goals are achieved. The approach was originally defined for evaluating defects for a set of
projects in the NASA Goddard Space Flight Center environment.
The result of the application of the Goal Question Metric approach application is the
specification of a measurement system targeting a particular set of issues and a set of rules
for the interpretation of the measurement data. The resulting measurement model has three
levels:
1. Conceptual level (GOAL): A goal is defined for an object, for a variety of reasons,
with respect to various models of quality, from various points of view, relative to a
particular environment. Objects of measurement are
Products: Artifacts, deliverables and documents that are produced during
the system life cycle; E.g., specifications, designs, programs, test suites.
Processes: Software related activities normally associated with time; E.g.,
specifying, designing, testing, interviewing.
Resources: Items used by processes in order to produce their out uts; E.g.,
p
personnel, hardware, software, office space.
2. Operational level (QUESTION): A set of questions is used to characterize the way
the assessment/achievement of a specific goal is going to be performed based on
some characterizing model. Questions try to characterize the object of
measurement (product, process, resource) with respect to a selected quality issue
and to determine its quality from the selected viewpoint.
3. Quantitative level (METRIC): A set of data is associated with every question in
order to answer it in a quantitative way. The data can be
Objective: If they depend only on the object that is being measured and not
on the viewpoint from which they are taken; E.g., number of versions of a
document, staff hours spent on a task, size of a program.
Subjective: If they depend on both the object that is being measured and
the viewpoint from which they are taken; E.g., readability of a text, level of
user satisfaction.

The complete Goal Question Metric model is as follows:

THE GOAL QUESTION METRIC PROCESS
A GQM model is developed by identifying a set of quality and/or productivity goals, at
corporate, division or project level; e.g., customer satisfaction, on-time delivery, improved
performance. From those goals and based upon models of the object of measurement, we
derive questions that define those goals as completely as possible. For example, if it is to
characterize a software system (e.g., an electronic mail package, a word processor) with
respect to a certain set of quality issues (e.g., portability across architectures), then a quality
model of the product must be chosen that deals with those issues (e.g., list of functional
features that can be implemented in different architectures). The next step consists in
specifying the measures that need to be collected in order to answer those questions, and to

track the conformance of products and processes to the goals. After the measures have been
specified, we need to develop the data collection mechanisms, including validation and
analysis mechanisms.

CONCLUSION
In summary, the Goal Question Metric approach is a mechanism for defining and
interpreting operational and measurable software. It can be used in isolation or, better,
within the context of a more general approach to software quality improvement.
Figure below outlines the basic roles and flows of information for this model.

UNIT II SOFTWARE QUALITY ASSURANCE

Software Quality Assurance (SQA) consists of a means of monitoring the software
engineering processes and methods used to ensure quality. It does this by means of audits of
the quality management system under which the software system is created. These audits
are backed by one or more standards, usually ISO 9000.
It is distinct from software quality control which includes reviewing requirements
documents, and software testing. SQA encompasses the entire software development
process, which includes processes such as software design, coding, source code control,
code reviews, change management, configuration management, and release management.
Whereas software quality control is a control of products, software quality assurance is a
control of processes.
Software quality assurance is related to the practice of quality assurance in product
manufacturing. There are, however, some notable differences between software and a
manufactured product. These differences stem from the fact that the manufactured product
is physical and can be seen whereas the software product is not visible. Therefore its
function, benefit and costs are not as easily measured. What's more, when a manufactured
product rolls off the assembly line, it is essentially a complete, finished product, whereas
software is never finished. Software lives, grows, evolves, and metamorphoses, unlike its
tangible counterparts. Therefore, the processes and methods to manage, monitor, and
measure its ongoing quality are as fluid and sometimes elusive as are the defects that they
are meant to keep in check.
Tasks
Quality Engineering
the activity consisting of the cohesive collection of all tasks that are primarily
performed to ensure and help continually improve the quality of an endeavor‘s
process and work products
Goals
The typical goals of quality engineering are to:
Ensure that the necessary levels of quality are achieved.
Make the achievement of quality predictable and repeatable.
Minimize endeavor, organizational, and personal risks due to poor quality.
Objectives
The typical objectives of quality engineering are to:
Define what quality means on the endeavor in terms of a quality model defining
quality factors and quality sub factors.

Plan the quality tasks including helping the requirements team determine and
specify the quality requirements and associated quality factors (attributes) and
quality metrics.
Assure the quality of the process used by the endeavor.
Thus, quality assurance is concerned with fulfilling the quality requirements and
achieving the quality factors of the endeavor‘s process.
―Are we building the products right?‖
Control the quality of the work products delivered during the endeavor.
Thus, quality control is concerned with fulfilling the quality requirements and
achieving the quality factors of the endeavor‘s work products.
―Are we building the right products?‖
Examples
Examples of quality engineering based on scope include:
Application Quality Engineering
Business Quality Engineering
Contact Center Quality Engineering
Data Center Quality Engineering
Preconditions
Quality engineering typically may begin when the following preconditions hold:
The endeavor is started.
The quality team is initially staffed and trained in quality engineering.
Completion Criteria
Quality engineering is typically complete when the following post conditions hold:
The endeavor is complete.

Tasks
The following diagram illustrates the relationships between the quality tasks:

Plan
The purpose of this Software Quality Assurance Plan (SQAP) is to define the techniques,
procedures, and methodologies that will be used at the Center for Space Research (CSR) to
assure timely delivery of the software that meets specified requirements within project
resources.
The use of this plan will help assure the following: (1) That software development,
evaluation and acceptance standards are developed, documented and followed. (2) That the
results of software quality reviews and audits will be given to appropriate management
within CSR. This provides feedback as to how well the development effort is conforming to
various CSR development standards. (3) That test results adhere to acceptance standards.

Teams
The SQA team shall check that the quality is maintained during the project and that the
proper quality procedures are being followed, discovered problems are reported to the
Project Management. The members of the project team must work according to the part(s)
of the SQAP that applies to their specific task.
The tasks of the SQA team
For the first phase of the project (UR), the SQA team must see to it that the following
documents are properly reviewed internally before they are submitted for an external
review.
The URD
The SQA team must check whether the URD:
o
o

contains a general description of the software that has to be developed;
contains requirements on the software to be developed as stated by the
client;
o contains constraints on the software to be developed;
o contains a priority list of the requirements.
The SPMP
The SQA team must check whether the goals of the project are clearly described. A
life cycle approach for the project must be defined. The SQA team must ensure that
the SPMP is realistic by checking:
o
o
o

the assumptions made during the planning of the project;
restrictions with respect to plan (e.g. availability of members);
external problems (e.g. delivery of PCs, interface card and drivers).
The SCMP
With respect to the SCMP, the SQA team has to check whether the document
provides procedures concerning:
o
o
o
o

CI identification
CI storage
CI change control
CI status indication

All documents must have a unique identifier and backups must be made at least
once every three days.
The SQAP
With respect to the SQAP, the SQA team must check wether the SQAP contains:

o
o
o
o

Project standards
Review procedures
Problem reporting procedures
Responsibilities of the project members with respect to quality assurance

Tasks during SR phase
For the second phase of the project (SR), the SQA team must see to it that the following
review.
The SRD
The SQA team must check whether the SRD:
o

contains requirements on the software to be developed, these requirements
must be based on the software requirements stated in the URD;
o contains constraints on the software to be developed, these constraints must
be based on the software constaint in the URD;
o contains a priority list of the requirements.
o contains a traceability matrix.
The SPMP-SR
The SQA team must ensure that the SPMP is realistic by checking:
o
o
o

the assumptions made during the planning;
restrictions with respect to the planning (e.g. availability of members);
external problems (e.g. external software/code).
The SCMP-SR
Whith respect to the SCMP, the SQA team must check wether the SCP contains:
o the additional baselines.
The SQAP-SR

o

the Tasks of the SQA team during the SR phase.

Tasks during AD phase
For the third phase of the project (AD), the SQA team must see to it that the following
review.
The ADD
The SQA team must check whether the ADD:
o

contains an architectural design of the software to be developed, this design
must describe a logical model and the interfaces between the different
classes;
o contains pre and post conditions of the methods in the locical model;
o contains a tracibility matrix where the design is checked to the software
requirements in the SRD.
The SPMP-AD
The SQA team must ensure that the SPMP is realistic by checking:
o
o
o

the assumptions made during the planning;
restrictions with respect to the planning (e.g. availability of members);
external problems.
The SCMP-AD
Whith respect to the SCMP, the SQA team must check wether the SCMP contains:
o the additional baselines.
The SQAP-AD

o

the tasks of the SQA team during the AD phase.

characteristics of a good QA (Quality Assurnace) Engineer:
• Understanding of business approach and goals of the organization
• Understanding of entire software development process
• Strong desire for quality
• Establish and enforce SQA methodologies, processes and Testing Strategies
• Judgment skills to assess high-risk areas of application

• Communication with Analysis and Development team
• Report defects with full evidence
• Take preventive actions
• Take actions for Continuous improvement
• Reports to higher management
• Say No when Quality is insufficient
• Work Management
• Meet deadlines

Documentation:
Project documentation may include many kinds of documents (e.g., plans, task reports,
development products, problem reports, phase summary reports). Project size, criticality
(i.e., the severity of the consequence of failure of the system), and complexity are some
features that may affect the amount of documentation a project should need. For example,
the design documentation may consist of a single document describing both the system
architecture and the detailed modules or it may consist of separate documents for the
architecture and subsystems. The purpose of this section is not to specify how many
documents should be required. Rather, this section identifies the information content needed
for any project and the timeliness of requirements so that the information can be used by the
vendor, the utility, and the NRC reviewers. Because the NRC reviewers cannot determine
the characteristics of the software product without substantial technical specifications,
project plans, and reports, NRC should specify the technical products of the vendor that the
utility must provide NRC.
Review:
The reviewers will also need to evaluate the installation package, which consists of
installation procedures, installation medium (e.g., magnetic tape), test case data used to
verify installation, and expected output from the test cases. In some instances, the product
may already be installed in the utility. NRC should request documentation on the results of
installation and acceptance testing.

UNIT III QUALITY CONTROL AND RELIABILITY
Reliability and Quality Control:
Although the terms reliability and quality are often used interchangeably, there is a
difference between these two disciplines. While reliability is concerned with the
performance of a product over its entire lifetime, quality control is concerned with the
performance of a product at one point in time, usually during the manufacturing process. As
stated in the definition, reliability assures that components, equipment and systems function
without failure for desired periods during their whole design life, from conception (birth) to
junking (death). Quality control is a single, albeit vital, link in the total reliability process.
Quality control assures conformance to specifications. This reduces manufacturing
variance, which can degrade reliability. Quality control also checks that the incoming parts
and components meet specifications, that products are inspected and tested correctly, and
that the shipped products have a quality level equal to or greater than that specified. The
specified quality level should be one that is acceptable to the users, the consumer and the
public. No product can perform reliably without the inputs of quality control because
quality parts and components are needed to go into the product so that its reliability is
assured.
Quality Tools:
Cause Analysis Tools
Tips and tools for the first step to improvement: identifying the cause of a problem or
situation.
Evaluation and Decision-Making Tools
Making informed decisions and choosing the best options with a simple, objective rating
system, and determining the success of a project.
Process Analysis Tools
How to identify and eliminate unnecessary process steps to increase efficiency, reduce
timelines and cut costs.
Seven Basic Quality Tools
These seven tools get to the heart of implementing quality principles.
Data Collection and Analysis Tools
How can you collect the data you need, and what should you do with them once they‘re
collected?
Idea Creation Tools
Ways to stimulate group creativity and organize the ideas that come from it.
Project Planning and Implementing Tools

How to track a project‘s status and look for improvement opportunities.
Seven New Management and Planning Tools
Ways to promote innovation, communicate information and successfully plan major
projects.

Ishikawa's seven basic tools for quality control are:








checklist (or check sheet),
Pareto diagram,
histogram,
scatter diagram,
run chart,
control chart, and
cause-and-effect diagram.

The following figure shows a simple representation of the tools.

Check sheet
A check sheet is a paper form with printed items to be checked. Its main purposes
are to facilitate gathering data and to arrange data while collecting it so the data can be
easily used later. Another type of check sheet is the check-up confirmation sheet. It is
concerned mainly with the quality characteristics of a process or a product. To distinguish
this confirmation check sheet from the ordinary data-gathering check sheet, we use the
term checklist. In most software development environments, the data-gathering aspect
is automated electronically and goes far beyond the data-gathering check sheet approach,
which has been used in manufacturing production.
Pareto diagram
A Pareto diagram is a frequency chart of bars in descending order; the frequency
bars are usually associated with types of problems. In software development, the X-axis
for a Pareto diagram is usually the defect cause and the Y-axis the defect count. By
arranging the causes based on defect frequency, a Pareto diagram can identify the few
causes that account for the majority of defects. It indicates which problems should be
solved first in eliminating defects and improving the operation. Pareto analysis is
commonly referred to as the 80–20 principle (20% of the causes account for 80% of the
defects), although the cause-defect relationship is not always in an 80–20 distribution.
Histogram
The histogram is a graphic representation of frequency counts of a sample
or a population. The X-axis lists the unit intervals of a parameter (e.g., severity level of
software defects) ranked in ascending order from left to right, and the Y-axis contains the
frequency counts. In a histogram, the frequency bars are shown by the order of the X
variable, whereas in a Pareto diagram the frequency bars are shown by order of the
frequency counts. The purpose of the histogram is to show the distribution characteristics
of a parameter such as overall shape, central tendency, dispersion, and skew ness. It
enhances understanding of the parameter of interest.
Scatter diagram
A scatter diagram vividly portrays the relationship of two interval variables. In a
cause - effect relationship, the X-axis is for the independent variable and the Y-axis for
the dependent variable. Each point in a scatter diagram represents an observation of both
the dependent and independent variables. Scatter diagrams aid data -based decision
making (e.g., if action is planned on the X variable and some effect is expected on
the Y variable).
Run chart
A run chart tracks the performance of the parameter of interest over time. The Xaxis is time and the Y-axis is the value of the parameter. A run chart is best used for
trend analysis, especially if historical data are available for comparisons with the current
trend.

An example of a run chart in software is the weekly number of open problems in the backlog; it
shows the development team's workload of software fixes.
Control chart
A control chart can be regarded as an advanced form of a run chart for situations where
the process capability can be defined. It consists of a central line, a pair of control limits (and
sometimes a pair of warning limits within the control limits), and values of the parameter of
interest plotted on the chart, which represent the state of a process. The X- axis is real time. If
all values of the parameter are within the control limits and show no particular tendency, the
process is regarded as being in a controlled state. If they fall outside the control limits or
indicate a trend, the process is considered out of con trol. Such cases call for causal analysis
and corrective actions are to be taken.
Cause-and-effect diagram
The cause-and-effect diagram, also known as the fishbone diagram, It was first used to
explain factors that affect the production of steel. It shows the relationship between a quality
characteristic and factors that affect that characteristic. Its layout resembles a fishbone, with the
quality characteristic of interest labeled at the fish head, and factors affecting the characteristics
placed where the bones are located. While the scatter diagram describes a specific bipartite
relationship in detail, the cause-and-effect diagram identifies all causal factors of a quality
characteristic in one chart.
1. Cause-and-effect diagram (also called Ishikawa or fishbone chart): Identifies many possible
causes for an effect or problem and sorts ideas into useful categories.
2. Check sheet: A structured, prepared form for collecting and analyzing data; a generic tool that
can be adapted for a wide variety of purposes.
3. Control charts: Graphs used to study how a process changes over time.
4. Histogram: The most commonly used graph for showing frequency distributions, or how often
each different value in a set of data occurs.
5. Pareto chart: Shows on a bar graph which factors are more significant.
6. Scatter diagram: Graphs pairs of numerical data, one variable on each axis, to look for a
relationship.
7. Stratification: A technique that separates data gathered from a variety of sources so that
patterns can be seen (some lists replace "stratification" with "flowchart" or "run chart").
Case tools:
Computer-aided software engineering (CASE) is the use of software tools to assist in the
development and maintenance of software. Tools used to assist in this way are known as CASE
Tools.
Some typical CASE tools are:
* Code generation tools
* Data modeling tools
* UML

* Refactoring tools
* QVT or Model transformation Tools
* Configuration management tools including revision control

Preventing, Discovering and Removing Defects
To reduce the number of defects delivered with a software project an organization can
engage in a variety of activities. While defect prevention is much more effective and efficient in
reducing the number of defects, most organization conduct defect discovery and removal.
Discovering and removing defects is an expensive and inefficient process. It is much more
efficient for an organization to conduct activities that prevent defects.
Defect Removal Efficiency
If an organization has no defect prevention methods in place then they are totally reliant on
defect removal efficiency.
1. Requirements Reviews up to 15% removal of potential defects
2. Design Reviews up to 30% removal of potential defects
3. Code Reviews up to 20% removal of potential defects
4. Formal Testing up to 25% removal of potential defects
In other words, if your organization is great at defect removal the maximum percentage of defects
your organization can expect to remove is 90%. If a software project is 100 function points, the
total number of maximum (or potential) defects could be 120. If you were perfect at defect
removal your project would still have up to 12 defects after all your defect discovery and removal
efforts. The far majority of organization would receive a B (medium) or even a D (poor) at defect
removal efficiency.
Activity
Perfect
Medium
Poor
Requirements Reviews
15% 5%
0%
Design Reviews
30% 15% 0%
Code Reviews
20% 10% 0%
Formal Testing
25% 15% 15%
Total Percentage Removed 90% 45% 15%
Defect Discovery and Removal
Size in Function Points
Totals Defects Remaining
Points Max Defects Perfect
Medium
Poor
100 120
12
66
102
200 240
24
132
204
500 600
60
330
510
1,000 1,200
120
660
1,020
2,500 3,000
300
1,650
2,550
5,000 6,000
600
3,300
5,100
10,000
12,000
1,200 6,600

10,200

20,000

24,000

2,000 13,200

20,400

An organization with a project of 2,500 function points and was about medium at defect discovery
and removal would have 1,650 defects remaining after all defect removal and discovery activities.
The calculation is 2,500 x 1.2 = 3,000 potential defects. The organization would be able to remove
about 45% of the defects or 1,350 defects. The total potential defects (3,000) less the removed
defects (1,350) equals the remaining defects of 1,650.
Defect Prevention
If an organization concentrates on defect prevention (instead of defect detection) then the
number of defects inserted or created is much less. The amount of time and effort required to
discover and remove this defects is much less also.
1. Roles and Responsibilities Clearly Defined up to 15% reduction in number of defects created
2. Formalized Procedures up to 25% reduction in number of defects created
3. Repeatable Processes up to 35% reduction in number of defects created
4. Controls and Measures in place up to 30% reduction in number of defects created
Imagine an organization with items 1 and 2 in place. A project with 100 function points would
have a potential of 120 defects, but since they have preventative measures in place, they can reduce
the number of potential defects by 48 (40% = 25% + 15%). That makes the potential number of
defects 72 compared to 120 with no preventative efforts. Assuming that an organization was
medium at defect discovery and removal they could remove 45% of the remaining defects or have
40 remaining when the project rolled to production.
Defect Removal
Max Defects Prevention
Medium
100
120
72
40
200
240
144
79
500
600
360
198
1,000
1,200
720
396
2,500
3,000
1,800
990
5,000
6,000
3,600
1,980
10,000
12,000
7,200
3,960
20,000
24,000
14,400
7,920
The above table represents the number of defects that an organization that does items 1 and 2
above and is medium at discovery and removal.
The problem for estimating defects is multidimensional. First the total number of defects must be
estimated. Second the impact of defect prevention needs to be understood and the estimated
number of defects adjusted. Third an assessment needs to be done to understand how many defects
can be discovered and removed by an organization.
Clearly, the fewer number of defects that an organization must discover and remove the better.
The way this is accomplished is by better process, a more stable organization and repeatable
processes. The focus of software organizations needs to be on defect prevention instead of defect
detection.

Software Reliability Models:
A proliferation of software reliability models have emerged as people try to understand the
characteristics of how and why software fails, and try to quantify software reliability. Over 200
models have been developed since the early 1970s, but how to quantify software reliability still
remains largely unsolved. Interested readers may refer to [RAC96], [Lyu95]. As many models as
there are and many more emerging, none of the models can capture a satisfying amount of the
complexity of software; constraints and assumptions have to be made for the quantifying process.
Therefore, there is no single model that can be used in all situations. No model is complete or even
representative. One model may work well for a set of certain software, but may be completely off
track for other kinds of problems.
Most software models contain the following parts: assumptions, factors, and a mathematical
function that relates the reliability with the factors. The mathematical function is usually higher
order exponential or logarithmic.
Software modeling techniques can be divided into two subcategories: prediction modeling and
estimation modeling. [RAC96] Both kinds of modeling techniques are based on observing and
accumulating failure data and analyzing with statistical inference
Rayleigh Model
Rayleigh model has been found to be most suitable for predicting reliability of software
product. It predicts the expected value of defect density at different stages of life cycle of the
project, once parameters like total number of defects or total cumulative defect rate and peak of the
curve in terms of unit of time for the curve are decided.
The nature of curve indicates the pattern of defect removal rate in the life cycle of the project. The
area bounded by the x-axis and the curve is the measure of total defects likely to be unearthed from
the software being developed.
Below is the plot of the Rayleigh Curve plotted for one of our projects:

Rayleigh Curve

Defects /KLOC

35
30

LLD
CODING

25
20

UT

15
10

IT

5

HLD
6.85

6.45

6.05

5.65

5.25

4.85

4.45

4.05

3.65

3.25

2.85

2.45

2.05

1.65

1.25

0.85

0.45

0.05

0

Life cycle stages

Fig 5: Rayleigh plot
The red line indicates the actual defect density observed as against the predicted values (brown
smooth curve) obtained through a theoretical model. Observed defect density closely matches with
the defect density predicted by the model.
The curve indicates the defect density at the time of system testing as 21 defects.

Reliability growth models for quality assessment:
Software reliability assessment is very important in developing a quality software product
efficiently. This paper discusses the quantitative measurement and assessment of software
reliability. The techniques are based on the software reliability growth models (SRGM's)
developed in Japan, which are characterized by nonhomogeneous Poisson processes. By making
the assumptions on which they are based more realistic, the models discussed here were designed
to describe a software error-detection process or a software failure-occurrence process during the
testing phase of software development. A summary of existing SRGM's is given, and the
maximum-likelihood estimations based on the SRGM's are discussed for software reliability data
analysis and software reliability assessment. Through the use of a software reliability assessment
tool that incorporates several leading SRGM's, examples of software reliability assessment are
given for some sets of observed test data taken from actual software projects.

Unit IV
Quality Management System
Topics covered
1. Concept of quality
2. QMS
3. Elements of qms
4. RAyleigh model
5. Reliability Growth Models
6. Complexity Metrics and Models
7. Customer Satisfaction Analysis

Concept of quality - historical background
The concept of quality as we think of it now first emerged out of The Industrial
Revolution. Previously goods had been made from start to finish by The same person or team of
people, with handcrafting and tweaking The product to meet 'quality criteria'. Mass production
brought huge teams of people together to work on specific stages of production where one person
would not necessarily complete a product from start to finish. In The late 1800s pioneers such as
Frederick Winslow Taylor and Henry Ford recognized The limitations of The methods being used
in mass production at The time and The subsequent varying quality of output. Taylor established
Quality Departments to oversee The quality of production and rectifying of errors, and Ford
emphasized standardization of design and component standards to ensure a standard product was
produced. Management of quality was The responsibility of The Quality department and was
implemented by Inspection of product output to 'catch' defects.
Application of statistical control came later as a result of World War production methods. Quality
management systems are The outgrowth of work done by W. Edwards Deming, a statistician, after
whom The Deming Prize for quality is named.
Quality, as a profession and The managerial process associated with The quality function, was
introduced during The second-half of The 20th century, and has evolved since then. Over this
period, few other disciplines have seen as many changes as The quality profession.
The quality profession grew from simple control, to engineering, to systems engineering. Quality
control activities were predominant in The 1940s, 1950s, and 1960s. The 1970s were an era of
quality engineering and The 1990s saw quality systems as an emerging field. Like medicine,
accounting, and engineering, quality has achieved status as a recognized profession
Quality Management System (QMS)
Quality Management System (QMS) can be defined as a set of policies, processes and
procedures required for planning and execution (production / development / service) in The core
business area of an organization. QMS integrates The various internal processes within The
organization and intends to provide a process approach for project execution. QMS enables The
organizations to identify, measure, control and improve The various core business processes that
will ultimately lead to improved business performance.

ELEMENTS OF QUALITY MANAGEMENT SYSTEMS
The standards of ISO 9000 detail 20 requirements for an organization's quality management
system in The following areas:
Management Responsibility
Quality System
Order Entry
Design Control
Document and Data Control
Purchasing
Control of Customer Supplied Products
Product Identification and Tractability
Process Control
Inspection and Testing Control of Inspection, Measuring, and Test Equipment
Inspection and Test Status
Control of Nonconforming Products
Corrective and Preventive Action
Handling, Storage, Packaging, and Delivery
Control of Quality Records
Internal Quality Audits
Training
Servicing
Statistical Techniques
The Rayleigh Model Framework
Perhaps The most important principle in software engineering is "do it right The first time."
This principle speaks to The importance of managing quality throughout The development
process. theinterpretation of The principle, in The context of software quality management, is
threefold:
The best scenario is to prevent errors from being injected into The development process.
When errors are introduced, improve The front end of The development process to remove
as many of them as early as possible. Specifically, in The context of The waterfall
development process, rigorous design reviews and code inspections are needed. In The
Cleanroom methodology, function verification by The team is used.
If The project is beyond The design and code phases, unit tests and any additional tests by
The developers serve as gatekeepers for defects to escape The front-end process before
The code is integrated into The configuration management system (The system library).
In other words, The phase of unit test or pre-integration test (The development phase prior
to system integration) is The last chance to do it right The "first time."

The Rayleigh model is a good overall model for quality management. It articulates The points on
defect prevention and early defect removal related to The preceding items. Based on The model,
if The error injection rate is reduced, The entire area under The Rayleigh curve becomes smaller,
leading to a smaller projected field defect rate. Also, more defect removal at The front end of The
development process will lead to a lower defect rate at later testing phases and during maintenance.
Both scenarios aim to lower The defects in The latter testing phases, which in turn lead to fewer
defects in The field. The relationship between formal machine-testing defects and field defects, as
described by The model, is congruent with The famous counterintuitive principle in software
testing by Myers (1979), which basically states that The more defects found during formal testing,
The more that remained to be found later. The reason is that at The late stage of formal testing,
error injection of The development process (mainly during design and code implementation) is
basically determined (except for bad fixes during testing). High testing defect rates indicate that
The error injection is high; if no extra effort is exerted, more defects will escape to The field.
If we use The iceberg analogy to describe The relationship between testing and field defect rates,
The tip of The iceberg is The testing defect rate and The submerged part is The field defect rate.
The size of The iceberg is equivalent to The amount of error injection. By The time formal
testing starts, The iceberg is already formed and its size determined. The larger its tip, The larger
The entire iceberg. To reduce The submerged part, extra effort must be applied to expose more of
The iceberg above The water. Figure 9.1 shows a schematic representation of The iceberg
analogy.
Figure 9.1. Iceberg Analogy—Error Injection, Testing Defects, and Latent Defects

Reliability Growth Models
Although reliability growth models are meant for reliability assessment, they are also useful
for quality management at the back end of the development process. Models developed from a
previous product or a previous release of the same product can be used to track the testing defects

of the current product. To have significant improvement, the defect arrival rate (or failure density)
of the current project must fall below the model curve. Figure 9.15 shows an example from a
systems software product developed at IBM Rochester. Each data point represents a weekly defect
arrival rate during the system test phase. The defect arrival patterns represented by the triangles
and circles indicate two later releases of the same product. Compared to the baseline model curve,
both new releases witnessed a significant reduction in defect rate during the system test phase.
Figure 9.15. Reliability Growth Model for Quality Management

As a second example, when another product was just about at the start of system testing, the PTR
arrival rates were unusually high compared to the model. It was clear that proceeding in a businessas-usual manner would not result in meeting the product's quality goal. A special quality
improvement program (QIP) was then pro-posed, evaluated, approved, and swiftly implemented.
The QIP involved five extra activities:
1. Blitz testing— "artistic" testing in stressful environments
2. Customer evaluation— customers conducting testing in the development laboratory
3. Code inspections— additional inspections of error-prone modules, especially routines that
are difficult to test such as the error recovery/exception handling routines
4. Design reviews— rereview of designs of suspect components and modules
5. Extension of system test— improvement of test suites and extension of testing schedules to
allow thorough final test execution

Because of the special QIP activities, the product ship date was delayed one month. As a result,
more than 250 would-be field defects were found and removed. The field quality of the product,
evidenced by field defect arrivals reported in later years, improved

Complexity Metrics and Models
Thus far the reliability and quality management models we have discussed are either at the
project or the product level. Both types of model tend to treat the software more or less as a black
box. In other words, they are based on either the external behavior (e.g., failure data) of the product
or the intermediate process data (e.g., type and magnitude of inspection defects), without looking
into the internal dynamics of design and code of the software. In this chapter we describe the
relationships between metrics about design and code implementation and software quality. The
unit of analysis is more granular, usually at the program-module level. Such metrics and models
tend to take an internal view and can provide clues for software engineers to improve the quality of
their work.
Reliability models are developed and studied by researchers and software reliability practitioners
with sophisticated skills in mathematics and statistics; quality management models are developed
by software quality professionals and product managers for practical project and quality
management. Software complexity research, on the other hand, is usually conducted by computer
scientists or experienced software engineers. Like the reliability models, many complexity metrics
and models have emerged in the recent past. In this chapter we discuss several key metrics and
models, and describe a real-life example of metric analysis and quality improvement.
. Lines of Code
The lines of code (LOC) count is usually for executable statements. It is actually a count of
instruction statements. The interchangeable use of the two terms apparently originated from
Assembler program in which a line of code and an instruction statement are the same thing.
Because the LOC count represents the program size and complexity, it is not a surprise that the
more lines of code there are in a program, the more defects are expected. More intriguingly,
researchers found that defect density (defects per KLOC) is also significantly related to LOC
count. Early studies pointed to a negative relationship: the larger the module size, the smaller the
defect rate. For instance, Basili and Perricone (1984) examined FORTRAN modules with fewer
than 200 lines of code for the most part and found higher defect density in the smaller modules.
Shen and colleagues (1985) studied software written in Pascal, PL/S, and Assembly language and
found an inverse relationship existed up to about 500 lines. Since larger modules are generally
more complex, a lower defect rate is somewhat counterintuitive. Interpretation of this finding rests
on the explanation of interface errors: Interface errors are more or less constant regardless of
module size, and smaller modules are subject to higher error density because of smaller
denominators.
More recent studies point to a curvilinear relationship between lines of code and defect rate: Defect
density decreases with size and then curves up again at the tail when the modules become very
large. For instance, Withrow (1990) studied modules written in Ada for a large project at Unisys
and confirmed the concave relationship between defect density (during formal test and integration
phases) and module size (Table 11.1). Specifically, of 362 modules with a wide range in size (from

fewer than 63 lines to more than 1,000), Withrow found the lowest defect density in the category
of about 250 lines. Explanation of the rising tail is readily available. When module size becomes
very large, the complexity increases to a level beyond a programmer's immediate span of control
and total comprehension. This new finding is also consistent with previous studies that did not
address the defect density of very large modules.
Experience from the AS/400 development also lends support to the curvilinear model. In the
example in Figure 11.1, although the concave pattern is not as significant as that in Withrow's
study, the rising tail is still evident.
Figure 11.1. Curvilinear Relationship Between Defect Rate and Module Size—AS/400 data

The curvilinear model
between size and defect density sheds new light on software quality engineering. It implies that
there may be an optimal program size that can lead to the lowest defect rate. Such an optimum may
depend on language, project, product, and environment; apparently many more empirical
investigations are needed. Nonetheless, when an empirical optimum is derived by reasonable
methods (e.g., based on the previous release of the same product, or based on a similar product by
the same development group), it can be used as a guideline for new module development.

Customer Satisfaction Analysis

What The clients really want is The interpretation and analysis of the data to provide actionable
information: What can we learn from it? What actions does it suggest that we take to improve
customer satisfaction with what we offer?
The analysis process starts by performing statistical tests to reveal relationships or differences in
customer ratings of The performance on different product and service attributes, and how they
affect overall satisfaction. We compare The performance to The peers, utilizing The Benchmark
database to describe how The performance rates on a relative basis. We identify The customers'
product and service priorities and we compare these to their perceptions of The performance
through Quadrant Analysis. We look for gaps in performance versus expectations in The search
for major opportunities for improvement.
We augment The analysis of the quantitative survey data with careful study of the qualitative
information – the comments and observations made by The customers. These are an invaluable
The of insight into the reasons behind their ratings. In most cases, The analysis is aimed at
identifying the key drivers of satisfaction – those product or service elements that are most closely
related to customer satisfaction.
The clients benefit from the perspective of The years of experience with a wide variety of clients.
Applying The knowledge, The goal is to deliver actionable results – information you can use to
create change that will improve The competitive position and The bottom line
ISO 9000
ISO 9000 is a family of standards for quality management systems. ISO 9000 is maintained by
ISO, the International Organization for Standardization and is administered by accreditation and
certification bodies. Some of the requirements in ISO 9001 (which is one of the standards in the
ISO 9000 family) would include

a set of procedures that cover all key processes in the business;
monitoring processes to ensure they are effective;
keeping adequate records;
checking output for defects, with appropriate corrective action where necessary;
regularly reviewing individual processes and the quality system itself for effectiveness; and
facilitating continual improvement
A company or organization that has been independently audited and certified to be in conformance
with ISO 9001 may publicly state that it is "ISO 9001 certified" or "ISO 9001 registered."
Certification to an ISO 9000 standard does not guarantee the compliance (and therefore the quality)
of end products and services; rather, it certifies that consistent business processes are being
applied.
Although the standards originated in manufacturing, they are now employed across a wide range of
other types of organizations. A "product", in ISO vocabulary, can mean a physical object, or
services, or software. In fact, according to ISO in 2004, "service sectors now account by far for the
highest number of ISO 9001:2000 certificates - about 31% of the total" [1]

UNIT V QUALITY STANDARD
Topics Covered:






Need for standards
ISO 9000 series
ISO 9000-3 series
CMM and CMMI
Six Sigma Concepts

1.Need for Standards:

Standardization (or standardisation) is the process of agreeing on technical standards. A stand
document that establishes uniform engineering or technical specifications, criteria, methods, processes, or p
Some standards are mandatory while others are voluntary. Some standards are defacto, meaning informal p
followed out of convenience, or dejure, meaning formal requirements. Formal standards bodies such
International Standard Organisation(ISO) or the American National Standard University are independen
manufacturers of the goods for which they publish standards.
The goals of standardization can be to help with independence of single suppliers
(commodification), compatibility, interoperability, safety, repeatability, or quality.
In social sciences, including economics, the idea of standardization is close to the solution for a
coordination problem, a situation in which all parties can realize mutual gains, but only by making
mutually consistent decisions. Standardization is the process for select better choices and ratificate
this consistent decisions, as an obtained standard.

Types of standardization process:
Emergence in de facto use: tradition (old standards on countries) and/or domination
(Microsoft ex.).
Fixed by a standard body:
o in an impositive process: usually in mandatory norms (like dictatorial Laws).
o in a consensus process: usually for voluntary standards
standardization is defined as: The development and implementation of concepts, doctrines,
procedures and designs to achieve and maintain the required levels of compatibility,
interchangeability or commonality in the operational, procedural, material, technical and
administrative fields to attain interoperability
2.ISO 9000 Series:
ISO 9000 is a family of standards for quality management systems. ISO 9000 is maintained
by ISO, the International Organization for Standardization and is administered by accreditation and
certification bodies.
ISO-9001 covers the entire process from product design through after sales service.
ISO-9002 covers only the manufacture (or a specific service such as a QC/QA laboratory) of the
product.
ISO-9003 covers the "final inspection" of the product only.
ISO-9004 is an "Internal Use" standard - it cannot be registered and is not subject to the third party
audits.

Software quality management lecture notes

ISO-9001:
Some of the requirements in ISO 9001 (which is one of the standards in the ISO 9000 family)
would include
a set of procedures that cover all key processes in the business;
monitoring processes to ensure they are effective;
keeping adequate records;
checking output for defects, with appropriate corrective action where necessary;
regularly reviewing individual processes and the quality system itself for effectiveness;
and
facilitating continual improvement

A company or organization that has been independently audited and certified to be in conformance
with ISO 9001 may publicly state that it is "ISO 9001 certified" or "ISO 9001 registered."
Certification to an ISO 9000 standard does not guarantee the compliance (and therefore the quality)
of end products and services; rather, it certifies that consistent business processes are being
applied.
Although the standards originated in manufacturing, they are now employed across a wide range of
other types of organizations. A "product", in ISO vocabulary, can mean a physical object, or
services, or software. In fact, according to ISO in 2004, "service sectors now account by far for the
highest number of ISO 9001:2000 certificates - about 31% of the total"

ISO 9001:2000 specifies requirements for a quality management system where an organization
1. needs to demonstrate its ability to consistently provide product that meets customer and
applicable regulatory requirements, and
2. aims to enhance customer satisfaction through the effective application of the system,
including processes for continual improvement of the system and the assurance of
conformity to customer and applicable regulatory requirements.
All requirements of this International Standard are generic and are intended to be applicable to all
organizations, regardless of type, size and product provided.

Where any requirement(s) of this International Standard cannot be applied due to the nature of an
organization and its product, this can be considered for exclusion.
Where exclusions are made, claims of conformity to this International Standard are not acceptable
unless these exclusions are limited to requirements within clause 7, and such exclusions do not
affect the organization's ability, or responsibility, to provide product that meets customer and
applicable regulatory requirements.

ISO-9002:

Developing products to a standard of consistent high quality, Scorpion Research is a
recognised leader in its field and proud to announce its achievement of the coveted ISO
9002
quality
certification.
The ISO 9002 standard is an emerging global standard for product and process quality,
adopted by 91 countries which comprise the International Organisation for
Standardisation
(ISO).
ISO 9002 is best known to European countries who use the certification as a means of
identifying companies dedicated to providing a customer with the best product and
service
possible.
Scorpion Research has always emphasised its quality through a continuous improvement
program complementing its other quality initiatives. Achieving ISO 9002 certification
further demonstrates commitment to a consistent level of quality recognised under
International
standards.
According to ISO assessors, the certification process is rigorous and only a minority of
the applicants who strive to become certified are successful on their first audit. Scorpion
Research is amongst this minority of organisations that passed the required certification
audit on the first assessment demonstrating its consistent emphasis towards a high quality
standard.
Certification is an ongoing process and Scorpion Research will undergo regular internal
and external audits by third party ISO assessors in order to retain its certification. The
ISO certification complements the quality program by establishing an externally verified
unbiased
process
of
self-monitoring
and
problem-solving.
Scorpion Research joins firms such as British Telecom, Sony, IBM, Compaq, Digital
Equipment, Xerox, Toshiba and Hewlett-Packard in ISO certification.
ISO-9003:
ISO
9003
This is the least complex and easiest to install of three standards in the ISO 9000 series. This
standard is for organizations that do not participate in design and development, purchasing or have

production controls. It is designed for organizations that only require final inspection and testing of
their products and services to ensure that they have met the specified requirements. This system is
generally only relevant to simple products and services. It is also an option for organizations that
cannot justify the expense of one of the other systems but still desire a quality management system
for their organization.
ISO-9004:
Your quality system must balance two needs:
Your customers' need to have quality
products at a reasonable price.
Your company's need to make quality
products at a reasonable cost.
3.ISO9000-3 for software development:
ISO 9000-3

4.4 Software development and design

4.4.1
General

Develop and document procedures to control the product design
and development process. These procedures must ensure that all
requirements are being met.

Software
development

Control your software development project and make
sure that it is executed in a disciplined manner.
Use one or more life cycle models to help
organize your software development project.
Develop and document your software development
procedures. These procedures should ensure that:
Software products meet all requirements.
Software development follows your:
Quality plan.
Development plan.

Software design

Control your software design process and make
sure that it is performed in a systematic way.
Use a suitable software design method.
Study previous software design projects
to avoid repeating old mistakes.
Design software that is:
Easy to test, install, use, and maintain.
Safe and reliable when failure could cause:
Human injury.
Property damage.
Environmental harm.
Develop and document rules to control:
Coding activities.
Naming conventions.
Commentary practices.

Programming languages.
Apply configuration management techniques to
document and control the use and review of all:
Analysis tools.
Design techniques.
Compilers and assemblers.
Train personnel in the use of such tools and techniques.
4.4.2
Design and
development
planning

Create design and development planning procedures.
Your product planning procedures should ensure that:
Plans are prepared for each design activity or phase.
Responsibility for implementing each plan,
activity, or phase is properly defined.
Qualified personnel are assigned to the
product design and development process.
Adequate resources are allocated to the
product design and development process.
Plans are updated, and circulated to the
appropriate participants, as designs change.

Software design
and development
planning

Prepare a software development plan. Your plan should be
documented and approved before it is implemented. Your plan
should control:
Technical activities.
Requirements analyses.
Design processes.
Coding activities.
Integration methods.
Testing techniques.
Installation work.
Acceptance testing.
Management activities.
Project supervision.
Progress reviews.
Reporting requirements.
Your software development plan should:
Define your project.
Identify related plans and projects.
List your project objectives.
Define project inputs and outputs.
Define inputs for each project activity.
Define outputs for each project activity.
Explain how your project will be organized.
Explain how your teams will be structured.
Explain who will be responsible for what.
Explain how subcontractors will be used.
Explain how project participants will interact.

Explain how all resources will be managed.
Discuss project risks and potential problems.
Identify important project assumptions.
Present your project schedule.
Define project phases and dependencies.
Specify project time lines and milestones.
Introduce your project budget.
Describe the work that will be done.
Describe each task.
Describe the inputs for each task.
Describe the outputs for each task.
Identify all relevant control strategies.
Identify all relevant standards and conventions.
Identify all relevant rules and regulations.
Identify all relevant practices and procedures.
Identify configuration management
practices.
Identify backup and recovery procedures.
Identify archiving procedures.
Identify all relevant methods and approaches.
Identify methods used to control
nonconforming products.
Identify methods used to control software
development software
Identify methods used to control virus
protection activities.
Identify all relevant tools and techniques.
Identify methods used to qualify all tools
and techniques.
Identify methods used to control all tools
and techniques.
4.4.3
Organizational
and technical
interfaces

Identify the groups who should be routinely involved in the
product design and development process, and ensure that their
design input is properly documented, circulated, and reviewed.
Make sure that your software development
plan or your subcontractors' plans:
Define how the responsibility for software development
will be distributed between all participants.
Define how technical information will be shared
and transmitted between all participants.
Explain how all project participants will provide input.
Explain how subcontractors will provide input during
design, installation, maintenance, and training.
Explain how regulatory authorities will provide input
during design, installation, maintenance, and training.

Explain how help desk staff will provide input during
Explain how other related projects will provide input
during design, installation, maintenance, and training.
Explain how end users will provide input during
Make sure that your customer has
accepted the responsibility to:
Cooperate and support your project.
Provide the information you need when you need it.
Resolve outstanding issues in a timely manner.
Make sure that your customer representative has
been given the responsibility and authority to:
Clarify requirements and expectations.
Answer questions and solve problems.
Make and implement agreements.
Approve plans and proposals.
Establish acceptance criteria.
Provide appropriate customer-supplied products.
Define and distribute authorities and responsibilities.
Schedule joint progress reviews. You and
your customer together should review:
Activities.
Your developmental activities.
Your customer's activities.
Your users' activities.
Training activities.
Conversion activities.
Results.
Results of verification activities.
Results of acceptance tests.
Results of conformance evaluations.
4.4.4
Design input

Develop procedures to ensure that all design-input requirements
are identified, documented, and reviewed; and that all design
flaws, ambiguities, contradictions, and deficiencies are resolved.
Design input requirements can be classified as follows:
Customer expectations.
Contractual conditions.
Statutory imperatives.
Regulatory requirements.
Environmental constraints.
Safety considerations.
Performance standards.
Functional specifications.
Descriptive prescriptions.
Aesthetic preferences.

Software
design input

Design input requirements should be specified by the customer.
However, sometimes the customer will expect you to develop the
design input specification. In this case, you should:
Prepare procedures that you can use to develop the design
input specification. These procedures should be
documented and should explain:
How interviews, surveys, studies, prototypes, and
demonstrations will be used to develop your designinput specification.
How you and your customer will formally agree:
To accept the official specification.
To accept changes to the official
specification.
How changes to specifications will be controlled.
How prototypes and product demonstrations will
be evaluated.
How system oriented input requirements will be
met through the use of hardware, software, and
interface technologies.
How reviews, evaluations, and other discussions
between you and your customer will be recorded.
Work closely with your customer in order to avoid
misunderstandings and to ensure that the specification
meets the customers needs.
Express your specification using terms that will make
it easy to validate during product acceptance.
Ask your customer to formally approve the
resulting design input specification.
Your design input specification may address the
following kinds of characteristics or requirements:
Functional requirements.
Reliability requirements.
Usability requirements.
Efficiency requirements.
Maintainability requirements.
Portability requirements.
Interface requirements.
Hardware interface requirements.
Software interface requirements.
Your design input specification may also need
to address the following kinds of requirements:
Operational requirements.
Safety requirements.
Security requirements.
Statutory requirements.

4.4.5

Develop procedures to control design outputs.

Design output

Design outputs are usually documents. They include
drawings, parts lists, process specifications, servicing
procedures, and storage instructions. These types of
documents are used for purchasing, production,
installation, inspection, testing, and servicing.
Design outputs must be expressed in terms that allow
them to be compared with design input requirements.
Design output documents must identify those aspects of
the product that are crucial to its safe and effective
operation. These aspects can include operating, storage,
handling, maintenance, and disposal requirements.
Design output documents must be reviewed
and approved before they are distributed.
Design outputs must be accepted only if
they meet official acceptance criteria.

Software
design output

Prepare design output documents using standardized
methods and make sure that your documents are correct
and complete.
Software design outputs can include design specifications,
source code, user guides, etc.

4.4.6
Design review

Develop procedures that specify how design reviews should be
planned and performed. Design review procedures should:
Be formally documented.
Ensure that reviews are recorded.
Ensure that representatives from all relevant
areas are involved in the process of review.

Software
design review

Plan and perform design reviews for software development
projects. Your reviews should ensure that all:
Design activities and results are reviewed.
Product nonconformities are identified and addressed.
Process deficiencies are identified and addressed.
Review conclusions and observations are recorded.

Software
design review
procedures

Develop and document design review procedures.
Your procedures should make sure that you:
Clarify exactly what is being reviewed.
Distinguish between different types of reviews.
Organize and schedule design review meetings.
Indicate when design reviews should be performed.
Maintain a record of all design review meetings.
Invite all appropriate groups to participate.
Invite customers to participate (when required).
Confirm that customers agree with your
results.

Allow design activities to continue only if all:
Deficiencies and nonconformities have been
addressed.
Risks and consequences have been assessed.
Your design review procedures may also:
Define the methods that should be used to ensure
that all rules and conventions are being followed.
Define what needs to be done to prepare
for a design review. This may include:
Defining roles.
Listing objectives.
Preparing an agenda.
Collecting documents.
Define what should be done during the review.
This may include:
Defining the guidelines that should be followed.
Defining the techniques that should be used.
Define the criteria that constitute a successful review.
Define the follow-up methods that should be used to
ensure that all outstanding issues will be addressed.
4.4.7
Design
verification

Develop procedures that specify how design outputs, at every
stage
of the product design and development process, should be
verified.
These procedures should:
Verify that outputs satisfy design-input requirements.
Ensure that objective evidence is used to verify outputs.
Ensure that all design verifications are recorded.
Ensure that all design documents are verified.
These design verification procedures may also:
Use alternative calculations to verify design outputs.
Use tests and demonstrations to verify outputs.
Compare design outputs with proven designs.

Software design
verification

Verify design outputs by:
Performing design reviews.
Performing demonstrations.
Evaluating prototypes.
Carrying out simulations.
Performing tests.
Maintain a record of design verifications.
Record verification results.
Record remedial actions.
Record action completions.
Accept design outputs for subsequent use only if they have been
properly verified and only if all remedial actions have been taken.

4.4.8
Design
validation

Software
design validation

Develop procedures that validate the assumption that your newly
designed products will meet customer needs. Develop design
validation procedures that:
Confirm that your new product performs properly
under all real-world operating conditions.
Confirm that your new product will meet every
legitimate customer need and expectation.
Ensure that validations are carried out early in the design
process whenever this will help guarantee that customer
needs will be met.
Prove that your product is ready for its intended
use before you ask your customer to accept it.
Accept validated products for subsequent use only if they
have been properly verified and only if all remedial actions
have been taken.
Maintain a record of design validations.
Record validation results.
Record remedial actions.
Record action completions.

4.4.9
Design changes

Develop procedures to ensure that all product design
modifications are
documented, reviewed, and formally authorized before the
resulting
documents are circulated and the changes are implemented.

Software
design changes

Develop procedures to control design changes that may
occur during
the product life cycle. Your procedure should ensure that
you:
Document the design change.
Evaluate the design change.
Justify the design change.
Verify the design change.
Approve the design change.
Implement the design change.
Monitor the design change.
Your configuration management process
may be used to control design changes.

4.CMM and CMMI

The Capability Maturity Model (CMM), also sometimes referred to as the Software CMM
(SW-CMM).The CMM is a process capability model based on software development organisation
processes/practices.
Though the CMM was retired in 1997 and has not been updated since, having been superseded by
CMMI (Capability Maturity Model Integration), it has been used as a generally applicable
model to assist in understanding the process capability maturity of organisations in diverse areas.
For example, software engineering, system engineering, project management, risk management,
system acquisition, information technology (IT) or personnel management, against a scale of five
maturity levels, namely: Initial, Repeatable, Defined, Managed and Optimized.
The Capability Maturity Model (CMM) is a process capability maturity model which aids in the
definiton and understanding of an organisation's processes. The CMM was originally used to
enable the assessment of software development processes.

Capability Maturity Model
• CMM model strives to achieve predictability and consistency as a
precursor to continuous improvements by following a set of process in a well
defined framework.
• Level 1 is Initial level
• Level 2 is repeatable which helps in achieving repeatability of performance
and quality should the organizations undertake a similar project again.
• Level 3 is defined level
• Level four is measured level.
• Level 5 is optimistic level , here people always work towards a target.
The Role of CMM
The role of CMM is increasing. This may be attributed to a number of factors:
 The maturity of the model itself
 The increasing general awareness of the need for externally recognized quality
standards

 The adoption of model by key software purchases such as national departments of
defense.
Advantages of CMM






It allows for improvement and evolution
Also used in conjunction with other quality standards
It highlights the defects as they occur.
The CMM prioritizes tasks for improvement
It provides a matrix for strengths and weakness

Schematic view of CMM

The maturity model seeks to measure how well these processes are carried out.There are
five stages to six measurement categories in subjectively rating an organization‘s quality
operation.
The five stages are:
 Uncertainty, where management is confused and uncommitted regarding quality
management tool
 Awakening, where management is beginning to recognize that quality
management can help
 Enlightenment, where the decision is made to conduct a formal quality
improvement programme
 Wisdom, where the company has the chance to make changes permanent (things
are basically quiet and people wonder why they used to have problems);
 Certainty, where quality management is considered an absolutely vital part of
company management.
The six measurement categories are:
 management understanding and attitude, characterized as ‗no comprehension of
quality as a management tool‘ at uncertainty and ‗an essential part of the compa ny
system‘ at certainty;
 quality organization status, characterized as hidden at uncertainty and a thought
leader/main concern at certainty;
 problem handling, which are fought when they occur at uncertainty and prevented
at certainty;
 cost of quality as percentage of sales, characterized as 20% at uncertainty and
2.5% at certainty;
 quality improvement actions, characterized as no organized activities at
uncertainty and a normal and continued activity at certainty;
 Summation of company quality posture, summarized as ‗we do not know why we
have problems with quality‘ at uncertainty and ‗we know why we do not • have
problems with quality‘ at certainty.
As a precursor to the maturity model itself, Radice et.al. (1985) working with Humphrey,
identified 12 process stages. The process stages were stages in the lifecycle:

Evolution of the CMM
Year

Version published

1987

Software process maturity framework

1987

Preliminary maturity questionnaire

1987

Characterizing the software process

1989

Managing the software process

1990

Draft version of CMM v0.2

1991

CMM v0.6 discussion

1991

CMM v1.0

1993

CMM v1.1

The CMMI is organized around a set of Process Areas (PAs). The PAs are divided into
groups associated with what are called maturity levels. In the CMMI, most basic
management practices are considered part of maturity level 2, while most software
engineering practices are associated with level 3. Level 4 is about process and product
quality management, while level 5 includes processes for process optimization and
technology change management.

An organization that has practices that meet all the goals of maturity level 2 is characterized
as a level 2 organization. An organization that cannot demonstrate that its practices meet all
the applicable level 2 goals is considered level 1 by default. An organization that meets the
goals of all the PAs at level 2 and level 3 is a level 3 maturity organization and so on.
The CMMI also offers an alternate approach call the "continuous representation", where
individual PA's are rated on a maturity scale of 1 - 5. This allows the organization the
flexibility to tailoring its maturity goals based on business needs for particular PA's.
As organizations move up the maturity ladder, they are more likely to produce higher
quality products with more predictable costs and cycle times. The higher levels are more
likely to correlate with higher productivity and shorter cycle times as well.
The idea of model based software improvement is very simple. First pick the applicable
Process Areas. Next perform an assessment of organizational practices relative to the
model. The organization practices do not have to conform exactly to the representative
practices included in the model. They just have to meet the stated goals of each PA. Based
on this comparison, assign a maturity level to the organization and produce a list of
strengths and weakness relative to the model.
The output of the assessment is used to prioritize areas for improvement. The idea is to
improve deficient practices at the lower maturity levels first and systematically move up in
maturity level over time using additional assessments to measure progress.
Focusing on the lowest level practices puts a firm foundation in place before tacking the
higher level processes and it give the organization time to internalize the changes. This
approach nicely avoids the twin problems of putting practices in place before required
supporting practices are available and trying to do too much too soon.
So model based improvement has a lot of very attractive features. One of the most
attractive ones is the level goal itself. The numerical level gives organizations a metric that
that can be easily understood, can used to measure progress, and can used to benchmark
against other organizations.
SEI has defined a formal appraisal methodology and provided a lead assessor training and
certification program. This means that assessment results, particular those obtained using a
third party assessor, will be reasonably consistent and can be used to benchmark against
other organizations. In fact SEI maintains a publicly accessible database of assessment
results giving the number of organizations at each level by industry and the average time
for an organization to improve from one level to the next.

6.Six Sigma Concept:
Six Sigma at many organizations simply means a measure of quality that strives for
near perfection. Six Sigma is a disciplined, data-driven approach and methodology for
eliminating defects (driving towards six standard deviations between the mean and the
nearest specification limit) in any process -- from manufacturing to transactional and from
product to service.

The term Six Sigma represents a stringent level of quality. It is a specific defect rate: 3.4
defective parts per million (ppm). It was made known in the industry by Motorola, Inc.,
Six Sigma has become an industry standard as an ultimate quality goal.
Sigma (s) is the Greek symbol for standard deviation.
As the following figure1 indicates, the areas under the curve of normal distribution
defined by standard deviations are constants in terms of percentages, regardless of the
distribution parameters.
The area under the curve as defined by plus and minus one standard deviation (sigma)
from the mean is
68.26%.
The area defined by plus/minus two standard deviations is 95.44%, and so forth. The area
defined by plus/minus six sigma is 99.9999998%. The area outside the six sigma area is
thus 100% -99.9999998% = 0.0000002%.

Fig. 1
The area within the six sigma limit as the percentage of defect-free parts and the area
outside the limit as the percentage of defective parts, it is found that six sigma is equal to
2 defectives per billion parts or 0.002 defective parts per million.

The interpretation of defect rate as it relates to the normal distribution will be clearer if
we include the specification.

Given the specification limits (which were derived from customers' requirements), our
purpose is to produce parts or products within the limits. Parts or products outside the
specification limits do not conform to requirements. If we can reduce the variations in the
production process so that the six sigma (standard deviations) variation of the production
process is within the specification limits, then we will have six sigma quality level.

Fig. 2 Specification Limits, Centered six sigma, and Shifted six sigma

The six sigma value of 0.002 ppm is from the statistical normal distribution. It assumes that
each execution of the production process will produce the exact distribution of parts or
products centered with regard to the specification limits. In reality, however, process shifts
and drifts always result from variations in process execution.
The maximum process shifts as indicated by research is 1.5 sigma. If we account for this
1.5-sigma shift in the production process, we will get the value of 3.4 ppm. Such shifting is
illustrated in the two lower panels of fig.2.
Given fixed specification limits, the distribution of the production process m ay shift to the
left or to the right. When the shift is 1.5 sigma, the area outside the specification limit on one
end is 3.4 ppm, and on the other it is nearly zero.
The slight difference between the centered six sigma and the shifted six sigma may imply
something significant. The former is practically equivalent to zero defects, which may invite
the debate whether it is feasible to achieve such a goal.
In order to reach six sigma, we have to improve the process. Specifically, we must reduce
process variations so that the six sigma variation is still within the specification limits.
The concept and approach of six sigma has been expanded and applied to the
improvement of management systems and total quality management.

The statistical representation of Six Sigma describes quantitatively how a process is performing.
To achieve Six Sigma, a process must not produce more than 3.4 defects per million
opportunities. A Six Sigma defect is defined as anything outside of customer specifications. A
Six Sigma opportunity is then the total quantity of chances for a defect. Process sigma can easily
be calculated using a Six-Sigma calculator.
The fundamental objective of the Six Sigma methodology is the implementation of a
measurement-based strategy that focuses on process improvement and variation reduction
through the application of Six Sigma improvement projects. This is accomplished through the
use of two Six Sigma sub-methodologies: DMAIC and DMADV. The Six Sigma DMAIC
process (define, measure, analyze, improve, control) is an improvement system for existing
processes falling below specification and looking for incremental improvement. The Six Sigma
DMADV process (define, measure, analyze, design, verify) is an improvement system used to
develop new processes or products at Six Sigma quality levels. It can also be employed if a
current process requires more than just incremental improvement. Both Six Sigma processes are
executed by Six Sigma Green Belts and Six Sigma Black Belts, and are overseen by Six Sigma
Master Black Belts.

Six Sigma is a set of practices originally developed by Motorola to systematically improve
processes by eliminating defects.A defect is defined as nonconformity of a product or service to
its specifications.
While the particulars of the methodology were originally formulated by Bill Smith at Motorola
in 1986, Six Sigma was heavily inspired by six preceding decades of quality improvement
methodologies such as quality control, TQM, and Zero Defects. Like its predecessors, Six Sigma
asserts the following:
Continuous efforts to reduce variation in process outputs is key to business success
Manufacturing and business processes can be measured, analyzed, improved and
controlled
Succeeding at achieving sustained quality improvement requires commitment from the
entire organization, particularly from top-level management
Sigma (the lower-case Greek letter σ) is used to represent standard deviation (a measure of
variation) of a population (lower-case 's', is an estimate, based on a sample). The term "six sigma
process" comes from the notion that if one has six standard deviations between the mean of a
process and the nearest specification limit, there will be practically no items that fail to meet the
specifications. This is the basis of the Process Capability Study, often used by quality
professionals. The term "Six Sigma" has its roots in this tool, rather than in simple process
standard deviation, which is also measured in sigmas. Criticism of the tool itself, and the way
that the term was derived from the tool, often sparks criticism of Six Sixma.

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